{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Simulation: The Brier score under administrative censoring\n",
    "\n",
    "As discussed in the paper by [Kvamme and Borgan (2019)](https://arxiv.org/pdf/1912.08581.pdf), under administrative censoring, the IPCW Brier score can be biased if the covariates contain information closely connected to some of the censoring times.\n",
    "The proposed Administrative Brier score can be a useful alternative in these cases.\n",
    "\n",
    "We here reproduce one of the simulations from Section [SOMETHING] to show a scenario where the IPCW Brier has a clear bias and fails to rank the performance of two methods correctly. While the administrative Brier score still works as expected.\n",
    "The dataset `sac_admin5` contains the simulations, but is a little larger than the one in the paper.\n",
    "\n",
    "We will compare the `LogisticHazard` method with the `BCESurv`, where the latter is essentially as set of binary classifiers that disregards individuals as they are censored.\n",
    "\n",
    "**The following is not an introduction to the topic, so we recommend the reader to also look at the paper to understand the points made here.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn_pandas import DataFrameMapper\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import torch\n",
    "import torchtuples as tt\n",
    "from pycox.datasets import sac_admin5\n",
    "from pycox.models import LogisticHazard, BCESurv\n",
    "from pycox.evaluation import EvalSurv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(1234)\n",
    "_ = torch.manual_seed(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = sac_admin5.read_df()\n",
    "df_test = df_train.sample(frac=0.4)\n",
    "df_train = df_train.drop(df_test.index)\n",
    "df_val = df_train.sample(frac=1/3)\n",
    "df_train = df_train.drop(df_val.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20000, 10000, 20000)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tt.tuplefy(df_train, df_val, df_test).lens()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature transforms\n",
    "\n",
    "The dataset has covariates: `x0, ..., x23`, and we will standardize all covariates.\n",
    "Here we use the `sklearn_pandas.DataFrameMapper` to make feature mappers, but this has nothing to do the the `pycox` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(df_train, df_val, df_test, n_durations=50):\n",
    "    cols = list(df_train.columns.drop(['duration', 'event', 'duration_true', 'event_true', 'censoring_true']))\n",
    "\n",
    "    x_mapper = DataFrameMapper([([col], StandardScaler()) for col in cols])\n",
    "    x_train = x_mapper.fit_transform(df_train).astype('float32')\n",
    "    x_val = x_mapper.transform(df_val).astype('float32')\n",
    "    x_test = x_mapper.transform(df_test).astype('float32')\n",
    "\n",
    "    labtrans = LogisticHazard.label_transform(n_durations)\n",
    "    get_dur_ev = lambda df: (df['duration'].values.astype('float32'), df['event'].values.astype('float32'))\n",
    "\n",
    "    y_train = labtrans.fit_transform(*get_dur_ev(df_train))\n",
    "    y_val = labtrans.transform(*get_dur_ev(df_val))\n",
    "    y_test = labtrans.transform(*get_dur_ev(df_test))\n",
    "\n",
    "    train = tt.tuplefy(x_train, y_train)\n",
    "    val = tt.tuplefy(x_val, y_val)\n",
    "    test = tt.tuplefy(x_test, y_test)\n",
    "    return train, val, test, labtrans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We transform the covariates and targets so they can be used by the Logistic-Hazard and BCE method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 217 ms, sys: 17.7 ms, total: 235 ms\n",
      "Wall time: 277 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "train, val, test, labtrans = preprocess(df_train, df_val, df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_net(train, labtrans, n_layers=4, n_nodes=32, dropout=0.1):\n",
    "    in_features = train[0].shape[1]\n",
    "    out_features = labtrans.out_features\n",
    "    num_nodes = [n_nodes] * n_layers\n",
    "    net = tt.practical.MLPVanilla(in_features, num_nodes, out_features,\n",
    "                                  dropout=dropout)\n",
    "    return net"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit_and_predict(model_class, train, val, test, labtrans, lr=0.01, n_itp=20):\n",
    "    net = make_net(train, labtrans)\n",
    "    model = model_class(net, tt.optim.AdamWR(lr, cycle_eta_multiplier=0.8), duration_index=labtrans.cuts)\n",
    "    log = model.fit(*train, 256, 128, verbose=False, val_data=val,\n",
    "                    callbacks=[tt.cb.EarlyStoppingCycle()])\n",
    "    surv = model.interpolate(n_itp).predict_surv_df(test[0])\n",
    "    return surv, model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fit a Logistic-Hazard to the data set, and make survival predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 49s, sys: 8.04 s, total: 3min 57s\n",
      "Wall time: 1min 33s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "surv, model = fit_and_predict(LogisticHazard, train, val, test, labtrans, lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = model.log.to_pandas().iloc[1:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We fit the BCE method to the data set, and make survival predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3min 32s, sys: 6.8 s, total: 3min 38s\n",
      "Wall time: 1min 20s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "surv_bce, model_bce = fit_and_predict(BCESurv, train, val, test, labtrans, lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = model_bce.log.to_pandas().iloc[1:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluation\n",
    "\n",
    "For evaluation we will compare scores on the full uncensored data set, with the IPCW Brier score and the administrative Brier score on a test set with the same censoring distribution as the training set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "durations = df_test['duration'].values\n",
    "events = df_test['event'].values\n",
    "durations_true = df_test['duration_true'].values\n",
    "events_true = df_test['event_true'].values\n",
    "censor_durations = df_test['censoring_true'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ev_true = EvalSurv(surv, durations_true, events_true, 'km')\n",
    "ev_bce_true = EvalSurv(surv_bce, durations_true, events_true, 'km')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "time_grid = np.linspace(0, 100, 100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncensored test set\n",
    "\n",
    "For the uncensored test set, we see that the BCE method is clearly much worse then the Logistic-Hazard. This is expected as the BCE method simply disregards censored individuals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ev_true.brier_score(time_grid).rename('Logistic-Hazard').plot()\n",
    "ev_bce_true.brier_score(time_grid).rename('BCE').plot()\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.11843521411909225, 0.2087353025907521)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev_true.integrated_brier_score(time_grid), ev_bce_true.integrated_brier_score(time_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### IPCW Brier score with Kaplan-Meier censoring estimates\n",
    "\n",
    "We see that the IPCW Brier score actually considers the BCE method better thn the Logistic-Hazard. We know from the uncensored test set that this is the wrong conclusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "ev = EvalSurv(surv, durations, events, 'km', censor_durations)\n",
    "ev_bce = EvalSurv(surv_bce, durations, events, 'km', censor_durations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ev.brier_score(time_grid).rename('Logistic-Hazard').plot()\n",
    "ev_bce.brier_score(time_grid).rename('BCE').plot()\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.1174541826311283, 0.10952409721115865)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev.integrated_brier_score(time_grid), ev_bce.integrated_brier_score(time_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Administrative Brier Score\n",
    "\n",
    "The administrative Brier score correctly finds that the estimates of the BCE method is works than those of the Logistic-Hazard."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ev.brier_score_admin(time_grid).rename('Logistic-Hazard').plot()\n",
    "ev_bce.brier_score_admin(time_grid).rename('BCE').plot()\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.11781944742706693, 0.1225675342139344)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev.integrated_brier_score_admin(time_grid), ev_bce.integrated_brier_score_admin(time_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Estimating the  censoring distribution\n",
    "\n",
    "Instead of using Kaplan-Meier estimates of the censoring distribution in the IPCW Brier score, we can use a methods that allows for covariate-dependent estimation.\n",
    "\n",
    "The censoring distribution is, in itself, a survival process, but with opposite labels of the regular survival process.\n",
    "We can therefore estimate the censoring distribution with a survival model, but first we need to swap the labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "censor_dfs = (tt.tuplefy(df_train, df_val, df_test)\n",
    "              .apply(lambda x: x.assign(event=lambda s: 1 - s['event'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 0., 1.], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "censor_dfs[0]['event'].values[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 1., 0.], dtype=float32)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train['event'].values[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use a Logistic-Hazard model for estimating the censoring distribution. It has the same configurations as the survival model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 1min 51s, sys: 3.98 s, total: 1min 55s\n",
      "Wall time: 46.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "censor_data = preprocess(*censor_dfs)\n",
    "censor_surv, censor_model = fit_and_predict(LogisticHazard, *censor_data, lr=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a32dd8890>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "censor_model.log.to_pandas().iloc[1:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "ev_cens = EvalSurv(surv, durations, events, censor_surv)\n",
    "ev_bce_cens = EvalSurv(surv_bce, durations, events, censor_surv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The IPCW Brier score with the new censoring estimates clearly considers the BCE methods better than the Logistic-Hazard survival model.\n",
    "So, only the Administrative Brier scores was able to correctly rank the two methods."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ev_cens.brier_score(time_grid).rename('Logistic-Hazard').plot()\n",
    "ev_bce_cens.brier_score(time_grid).rename('BCE').plot()\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.10889572553012469, 0.0985783516744046)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev_cens.integrated_brier_score(time_grid), ev_bce_cens.integrated_brier_score(time_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By further investigating the censoring estimates, we find that it has a very high concordance and a very small brier score.\n",
    "This confirms that we are almost able to identify the censoring times, as discussed in the paper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.9881688231010213, 0.01184456795207496)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev_cens.censor_surv.concordance_td(), ev_cens.censor_surv.add_km_censor().integrated_brier_score(time_grid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Binomial log-likelihood\n",
    "\n",
    "We find the same patterns for the (negative) binomial log-likelihood"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ev_true.nbll(time_grid).rename('Logistic-Hazard').plot()\n",
    "ev_bce_true.nbll(time_grid).rename('BCE').plot()\n",
    "plt.title('Uncensored')\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.36522008829959496, 1.246794590234243)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev_true.integrated_nbll(time_grid), ev_bce_true.integrated_nbll(time_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ev.nbll(time_grid).rename('Logistic-Hazard').plot()\n",
    "ev_bce.nbll(time_grid).rename('BCE').plot()\n",
    "plt.title('IPCW with Kaplan-Meier')\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.36138029672004307, 0.3369783440979498)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev.integrated_nbll(time_grid), ev_bce.integrated_nbll(time_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ev.nbll_admin(time_grid).rename('Logistic-Hazard').plot()\n",
    "ev_bce.nbll_admin(time_grid).rename('BCE').plot()\n",
    "plt.ylim(0, 0.6)\n",
    "plt.title('Administrative scores')\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.36220632960993066, 0.3855116022041178)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev.integrated_nbll_admin(time_grid), ev_bce.integrated_nbll_admin(time_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ev_cens.nbll(time_grid).rename('Logistic-Hazard').plot()\n",
    "ev_bce_cens.nbll(time_grid).rename('BCE').plot()\n",
    "plt.title('IPCW with Logistic-Hazard')\n",
    "_ = plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.33639969217229554, 0.3037243121488582)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev_cens.integrated_nbll(time_grid), ev_bce_cens.integrated_nbll(time_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
